We investigate the problem of target object information collection in multi-tagged RFID systems. Different from its single-tagged peers, the multi-tagged RFID scenario introduces three new challenges: 1) Tags on the same object carry the same information, so reusing single-tagged algorithms causes unnecessary redundancy; 2) The transition of objects from being tagged one to multiple tags leads to an upsurge in slot collisions; 3) Gathering information from more tags necessitates more downlink transmission, making it hard to limit broadcast information while ensuring time-efficient information collection. To tackle these technical challenges, we propose an efficient information collection algorithm, called Backtracking Collision Peeling (BCP), featuring three key techniques. First, BCP selects a single time slot (allowing even collision slots) for each target object to convey its information. This approach bypasses the need for a high-latency collision elimination process, thereby significantly reducing time overhead. Second, by exploiting dependencies among the selected slots, BCP recovers object information in complex collision slots using object information from already resolved slots, offering a novel and effective solution for handling signal collisions. Third, to minimize downlink transmission cost, BCP polls tags by transmitting only incremental changes between polling vectors, rather than the complete vectors. We further improve performance with E-BCP by enhancing the utilization of collision slots, thereby reducing the number of required polling rounds. Experiments show BCP and E-BCP outperform existing methods by at least$35\%$in aggregate execution time, while also exhibiting stronger stability and robustness.

We study the problem of target object information collection in multi-tagged COTS RFID systems. Unlike its singletagged peers, the multi-tagged COTS RFID scenario poses new challenges in devising information collection algorithms: 1) Tags attached to the same object carry identical information. Hence, reusing single-tagged information collection algorithms leads to unnecessary redundancy; 2) Multi-tagged RFID systems are often deployed in applications where tags are vulnerable to damage. Such faulty tags may severely degrade the performance of information collection; 3) Most state-of-the-art information collection algorithms rely heavily on the hashing operation that is not seamlessly supported by the C1G2 standard, rendering these solutions inefficient and impractical, especially in largescale RFID systems. To tackle these technical challenges, this paper makes three contributions. First, we develop an efficient and compact tag pseudo-ID design, enabling the reader to select a single tag from each target object to collect information with only one SELECT command. Second, we construct a robust faulthandling mechanism capable of recognizing faulty tags without executing the entire slot. Third, armed with the above two techniques, we develop a novel information collection algorithm by leveraging the functionality offered by C1G2 to optimize the information collection sequence, thus minimizing the overall execution time. Empirical experiments on a COTS RFID system prototype demonstrate that our algorithm outperforms the best existing solution by 35-50% on average.

We study the batch writing problem in RFID systems, where the reader seeks the most time-efficient way to write information into a given subset of tags. The problem is analogous to the multicast problem in classical networks, but one-to-many transmission is not supported in COTS RFID systems. Driven by the technical challenge, this paper addresses the problem of designing batch writing algorithms for COTS RFID systems. We make three contributions. Firstly, we establish the minimal execution time for any batch writing algorithm, thus setting the theoretical performance limit. Secondly, we quantitatively compare and gauge the existing propositions applicable to our problem. Thirdly, we develop a novel batch writing algorithm with minimal 25% performance gain over the best state-of-the-art solution. Our key technicalities are designing an encoding scheme allowing the reader to efficiently perform batch writing and optimizing the batch writing sequence to minimize the overall execution time. We also perform extensive experiments to demonstrate the effectiveness of our algorithm.

We revisit the problem of missing tag identification in RFID networks by making three contributions. Firstly, we quantitatively compare and gauge the existing propositions spanning over a decade on missing tag identification. We show that the expected execution time of the best solution in the literature is$\Theta \left(N+\frac{(1-\alpha)^2(1-\delta)^2}{ \epsilon^2}\right)$, where$\delta$and$\epsilon$are parameters quantifying the required identification accuracy,$N$denotes the number of tags in the system, among which$\alpha N$tags are missing. Secondly, we analytically establish the expected execution time lower-bound foranymissing tag identification algorithm as$\Theta\left(\frac{N}{\log N}+\frac{(1-\delta)^2(1-\alpha)^2}{\epsilon^2 \log \frac{(1-\delta)(1-\alpha)}{\epsilon}}\right)$, thus setting the theoretical performance limit. Thirdly, we develop two novel missing tag identification algorithms with the expected execution time of$\Theta \left(\frac{\log\log N}{\log N}N+\frac{(1-\alpha)^2(1-\delta)^2}{ \epsilon^2}\right)$, reducing the time overhead by a factor of up to$\log N$over the best algorithm in the literature. The key technicality in our first algorithm is a novel data structure termed as collision-partition tree (CPT), built on a subset of bits in tag pseudo-IDs, leading to a more balanced tree structure and reducing the time complexity in parsing the entire tree. To further improve time efficiency, our second algorithm integrates multiple CPTs to form a collision-partition forest (CPF), reducing both the number of slots and the quantity of information broadcasting.

We revisit the problem of missing tag identification in RFID networks by making three contributions. Firstly, we quantitatively compare and gauge the existing propositions spanning over a decade on missing tag identification. We show that the expected execution time of the best solution in the literature is $\Theta \left( {N + \frac{{{{(1 - \alpha )}^2}{{(1 - \delta )}^2}}}{{{\varepsilon ^2}}}} \right)$, where δ and ϵ are parameters quantifying the required identification accuracy, N denotes the number of tags in the system, among which αN tags are missing. Secondly, we analytically establish the expected execution time lower-bound for any missing tag identification algorithm as $\Theta \left( {\frac{N}{{\log N}} + \frac{{{{(1 - \delta )}^2}{{(1 - \alpha )}^2}}}{{{\varepsilon ^2}\log \frac{{(1 - \delta )(1 - \alpha )}}{\varepsilon }}}} \right)$, thus giving the theoretical performance limit. Thirdly, we develop a novel missing tag identification algorithm by leveraging a tree structure with the expected execution time of $\Theta \left( {\frac{{\log \log N}}{{\log N}}N + \frac{{{{(1 - \alpha )}^2}{{(1 - \delta )}^2}}}{{{\varepsilon ^2}}}} \right)$, reducing the time overhead by a factor of up to log N over the best algorithm in the literature. The key technicality in our design is a novel data structure termed as collision-partition tree (CPT), built on a subset of bits in tag pseudo-IDs, leading to more balanced tree structure and reducing the time complexity in parsing the entire tree.

With the development of 5G network and big data and the popularity of mobile intelligent devices, the opportunistic social network has been further developed. At present, several existing routing algorithms based on node similarity use the context information of the node to select the best relay node. However, most opportunistic social algorithms only consider the social properties of nodes and ignore the importance of the similarity of the moving trajectories of the nodes. The transmission opportunity of messages in the opportunity social network is generated by the movement of the nodes, so this feature must betaken into account in the designing of the routing algorithm. Therefore, this study proposes a routing algorithm based on the triangular fuzzy layer model and multi‐layer clustering for the opportunistic social network. In this study, the authors use the fuzzy analytic hierarchy process model to analyse the social similarity and trajectory similarity to determine the best message transmission node. This study compares the other four opportunistic social network routing algorithms in the simulation environment. In general, among the five routing algorithms, the transmission rate of the TFMC algorithm is the best. The average end‐to‐end delay and average network overhead are also the lowest.

With the development of big data, the authors have witnessed the great success of the mobile internet that the number of intelligent mobile devices (MDs) has increased dramatically and the data that needs to be transmitted grows exponentially. The traditional end‐to‐end communication mechanism in social networks is difficult to satisfy enormous communication demands. Therefore, opportunistic social networks proposed that message applications should choose relay nodes to perform effective data transmission processes. Currently, some routing algorithms that utilise contextual information associated with nodes need to handle heavy computing tasks and manage large numbers of messages, which usually results in higher network overhead and network latency. Furthermore, the computing power of MDs is usually limited, message carriers may not be able to quickly find the suitable relay node because of their insufficient computing power, resulting in higher network latency and network overhead. In this study, they construct a cooperative‐routing mechanism based on node classification and task allocation for opportunistic social networks. In their proposed strategy, the reliable relay nodes can be obtained by classifying nodes according to the social attributes of the nodes. The task of node classification will be uniformly assigned to other idle nodes according to their computing power.